
#import packages

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from math import pi
import warnings
warnings.filterwarnings('ignore')


#Load the data
df=pd.read_csv("players_20.csv")


#show information for data
df.info(null_counts=True)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18278 entries, 0 to 18277
Columns: 104 entries, sofifa_id to rb
dtypes: float64(16), int64(45), object(43)
memory usage: 14.5+ MB


df.dtypes

sofifa_id      int64
player_url    object
short_name    object
long_name     object
age            int64
               ...  
lb            object
lcb           object
cb            object
rcb           object
rb            object
Length: 104, dtype: object


df.shape

(18278, 104)


df.describe()


df.head()


df.tail()


df.columns

Index(['sofifa_id', 'player_url', 'short_name', 'long_name', 'age', 'dob',
       'height_cm', 'weight_kg', 'nationality', 'club',
       ...
       'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb', 'rcb', 'rb'],
      dtype='object', length=104)


df.duplicated().sum()

0


df.nunique()

sofifa_id     18278
player_url    18278
short_name    17354
long_name     18218
age              27
              ...  
lb               96
lcb             110
cb              110
rcb             110
rb               96
Length: 104, dtype: int64


uselesscolumn = ['dob','sofifa_id','player_url','long_name','body_type','real_face','nation_position','loaned_from','nation_jersey_number', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb']
# Drop unused columns
df = df.drop(uselesscolumn,axis = 1)


df.head()


df.columns

Index(['short_name', 'age', 'height_cm', 'weight_kg', 'nationality', 'club',
       'overall', 'potential', 'value_eur', 'wage_eur', 'player_positions',
       'preferred_foot', 'international_reputation', 'weak_foot',
       'skill_moves', 'work_rate', 'release_clause_eur', 'player_tags',
       'team_position', 'team_jersey_number', 'joined', 'contract_valid_until',
       'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic',
       'gk_diving', 'gk_handling', 'gk_kicking', 'gk_reflexes', 'gk_speed',
       'gk_positioning', 'player_traits', 'attacking_crossing',
       'attacking_finishing', 'attacking_heading_accuracy',
       'attacking_short_passing', 'attacking_volleys', 'skill_dribbling',
       'skill_curve', 'skill_fk_accuracy', 'skill_long_passing',
       'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed',
       'movement_agility', 'movement_reactions', 'movement_balance',
       'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength',
       'power_long_shots', 'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'mentality_composure', 'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes'],
      dtype='object')


# Check the missing values in the columns
missing_data = df.isnull().sum().sort_values(ascending=False)
missing_data = missing_data.reset_index(drop=False)
missing_data = missing_data.rename(columns={"index": "Columns", 0: "Value"})
missing_data['Proportion'] = (missing_data['Value']/len(df))*100

missing_data


sample = missing_data[missing_data['Proportion']>10]
fig = px.pie(sample, names='Columns', values='Proportion',
             color_discrete_sequence=px.colors.sequential.Viridis_r,
             title='Percentage of Missing values in Columns')
fig.update_traces(textposition='inside', textinfo='label')
fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
                  plot_bgcolor='rgba(0,0,0,0)',
                  font=dict(family='Cambria, monospace', size=12, color='#000000'))

fig.show()


# Replace NAN in others columns with 0
df = df.fillna(0)


df.isnull().sum().sort_values(ascending=False)

goalkeeping_reflexes          0
passing                       0
team_position                 0
team_jersey_number            0
joined                        0
                             ..
attacking_volleys             0
attacking_short_passing       0
attacking_heading_accuracy    0
attacking_finishing           0
short_name                    0
Length: 69, dtype: int64


# Creation of a BMI feature using Weight_kg & Height_cm
df['bmi'] = df['weight_kg'] // (df['height_cm']/100)**2


# Show the distribution of some numerical attributes by histogram
dist_subset = df[["overall", "age","potential",'value_eur', "wage_eur","release_clause_eur","height_cm","weight_kg", "pace"]]
dist_subset.hist(bins='auto', figsize=(15,10));


for col in dist_subset.columns:
    sns.boxplot(x = col, data = dist_subset) 
    plt.show()


# Give size for graph
plt.figure(figsize= (10,5))
# USe seaborn package to plot bar graph
ax = sns.countplot(x='age', data=df)

# Edit title , x label and y label
ax.set_title(label='Count of Players by age', fontsize=20)

ax.set_xlabel(xlabel='Age')
ax.set_ylabel(ylabel='Count');


# Give size for graph
plt.figure(figsize = (10,5))
# plot bar graph
df['nationality'].value_counts().head(50).plot.bar(color = 'purple')

# Edit title , x label and y label
plt.title('Players from different nationality present in FIFA-2020')
plt.xlabel('Country')
plt.ylabel('Count')
plt.show()


# plot pie chart
plt.figure(figsize= (20,10))
name =df['preferred_foot'].value_counts()
label = df['preferred_foot'].value_counts().index
explode = [0,0.2]
plt.pie(name,labels = label,shadow = True,autopct='%1.1f%%',explode =explode, radius = 0.9)
# Edit the title of graph
plt.title('Percentage of the preferred foot')
plt.legend();


# Give size for graph
plt.figure(figsize= (10,5))
# Use seaborn package to plot bar graph
ax = sns.countplot(x='bmi', data=df )
# Edit title, x label and y label 
ax.set_title(label='Count of Players by BMI')
ax.set_xlabel(xlabel='BMI(Body Mass Index)')
ax.set_ylabel(ylabel='Count');


# Select some columns to make analysis
metrics = ['overall','age', 'potential', 'value_eur', 'wage_eur','release_clause_eur',
           'attacking_finishing', 'dribbling', 'mentality_vision', 
           'power_shot_power', 'mentality_penalties', 'pace', 'passing', 'defending']
# for loop to show the name of player has the max value for all seleted columns
for i in metrics:
    best_Overall = df.loc[df[i] == df[i].max(), ['short_name', i]].values.tolist()
    print('Top', i, ': ', best_Overall[0][0], '-->', best_Overall[0][1])

Top overall :  L. Messi --> 94
Top age :  C. Muñoz --> 42
Top potential :  K. Mbappé --> 95
Top value_eur :  Neymar Jr --> 105500000
Top wage_eur :  L. Messi --> 565000
Top release_clause_eur :  L. Messi --> 195800000.0
Top attacking_finishing :  L. Messi --> 95
Top dribbling :  L. Messi --> 96.0
Top mentality_vision :  L. Messi --> 94
Top power_shot_power :  Cristiano Ronaldo --> 95
Top mentality_penalties :  M. Kruse --> 92
Top pace :  K. Mbappé --> 96.0
Top passing :  L. Messi --> 92.0
Top defending :  V. van Dijk --> 90.0


# Make descending sort the data by overall
top_rated = df.sort_values(by= ["overall"], ascending= False)


# use plotly to plot pie chart 
fig = px.pie(top_rated.head(20), names='club', title='Percentage of Clubs in Top 20 players',color_discrete_sequence=px.colors.sequential.RdBu)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()


# use plotly to plot pie chart 
fig = px.pie(top_rated.head(20), names='nationality', title='Percentage of Country in Top 20 players',color_discrete_sequence=px.colors.sequential.RdBu)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()


# Give size for graph
plt.figure(figsize=(10,5))
# Use seaborn package to plot bar graph
sns.countplot('age',data=top_rated.head(20))
# Edit title , x label , y label
plt.xlabel('Age')
plt.ylabel('Count')
plt.title(' Ages Contain top 20 players ',fontsize = 20);


# Select positions belong to defence
defence = ['LCB', 'RCB', 'LB', 'RB', 'CB', 'RWB', 'LWB']
sample = df.query('team_position in @defence') 

# Use plotly to plot pie chart 
fig = px.pie(sample, names='team_position',
             color_discrete_sequence=px.colors.sequential.Magma_r,  
             title='Percentage of players in Defender Role')
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
                  plot_bgcolor='rgba(0,0,0,0)',
                  font=dict(family='Cambria, monospace', size=12, color='#000000'))


# Select positions belong to the midfield
mid = ['CAM', 'RCM', 'CDM', 'LDM', 'RM', 'LCM', 'LM', 'RDM', 'RAM','CM', 'LAM']
sample = df.query('team_position in @mid')

# USe plotly to plot pie chart
fig = px.pie(sample, names='team_position',
             color_discrete_sequence=px.colors.sequential.Viridis_r,
             title='Percentage of players in Midfielder Role')
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
                  plot_bgcolor='rgba(0,0,0,0)',
                  font=dict(family='Cambria, monospace', size=12, color='#000000'))


# Select positions belong to attack
attack = ['RW', 'LW', 'ST', 'CF', 'LS', 'RS', 'RF', 'LF']
sample = df.query('team_position in @attack') 

# Use plotly to plot pie chart
fig = px.pie(sample, names='team_position',
             color_discrete_sequence=px.colors.sequential.Plasma_r,
             title='Percentage of players in Attacker Role')
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
                  plot_bgcolor='rgba(0,0,0,0)',
                  font=dict(family='Cambria, monospace', size=12, color='#000000'))


# Use plotly package to plot scatter plot
fig = go.Figure()

fig = go.Figure(data=go.Scatter(
    x = df['age'],
    y = df['overall'],
    mode='markers',
    marker=dict(
        color=df['overall'], 
        showscale=True
    ),
    text= df['short_name'],
))

fig.update_layout(title='Age vs Overall Rating',
                  xaxis_title='Age',
                  yaxis_title='Overall Rating')
fig.show()


# Use plotly package to plot scatter plot
fig = go.Figure()

fig = go.Figure(data=go.Scatter(
    x = df['bmi'],
    y = df['overall'],
    mode='markers',
    marker=dict(
        color=df['overall'], 
        showscale=True
    ),
    text= df['short_name'],
))

fig.update_layout(title='BMI vs Overall Rating',
                  xaxis_title='BMI',
                  yaxis_title='Overall Rating')
fig.show()


# Use plotly package to plot scatter plot
fig = go.Figure()

fig = go.Figure(data=go.Scatter(
    x = df['height_cm'],
    y = df['weight_kg'],
    mode='markers',
    marker=dict(
        color=df['weight_kg'], 
        showscale=True
    ),
    text= df['short_name'],
))

fig.update_layout(title='Height vs Weight',
                  xaxis_title='Height',
                  yaxis_title='Weight')
fig.show()


# Use plotly package to plot scatter plot
fig = go.Figure()

fig = go.Figure(data=go.Scatter(
    x = df['weight_kg'],
    y = df['pace'],
    mode='markers',
    marker=dict(
        color=df['pace'], 
        showscale=True
    ),
    text= df['short_name'],
))

fig.update_layout(title='pace vs Weight',
                  xaxis_title='Weight',
                  yaxis_title='pace')
fig.show()


# Use plotly package to plot scatter plot
plt.figure(figsize=(10, 5))
fig = go.Figure(data=go.Scatter(
    x = df['overall'],
    y = df['value_eur'],
    mode='markers',
    marker=dict(
        size=10,
        color=df['age'], #set color equal to a variable
        colorscale='Plasma', # one of plotly colorscales
        showscale=True
    ),
    text= df['short_name'],
))

fig.update_layout(title=' Scatter Plot (colored by Age) Overall Rating vs Value in Euros',
                  xaxis_title='Overall Rating',
                  yaxis_title='Value in Euros',
                  paper_bgcolor='rgba(0,0,0,0)',
                  plot_bgcolor='rgba(0,0,0,0)',
                  font=dict(family='Cambria, monospace', size=12, color='#000000'))

<Figure size 720x360 with 0 Axes>


sns.relplot(data = df, x = 'overall', y = 'potential', palette = 'viridis',
            hue='age',aspect=2, kind = 'line');


# Give size for the graph
plt.figure(figsize=(14, 7))

# Show the correlation between columns by plotting heatmap
df_heat=df[['age','overall','potential','wage_eur','value_eur','release_clause_eur','height_cm','weight_kg','bmi','pace', 'shooting',
            'passing', 'dribbling', 'defending', 'physic', 
       'attacking_finishing',
       'skill_ball_control', 'movement_sprint_speed', 'movement_balance']]
# Use seaborn package to plot heatmap
sns.heatmap(df_heat.corr(),  annot=True);


# Show some skills for Messi and Ronaldo
df2=df[['short_name','overall','potential','pace','shooting','passing','dribbling','defending','physic']]
rival = df2.head(2)
rival= rival.set_index('short_name')
rival.head()


# Finding the row values of Messi
values = rival.iloc[0].tolist()
values += values [:1]

# Finding the row values of Ronaldo
values2 = rival.iloc[1].tolist()
values2 += values2[:1]


# Plottin RadarChart to display the differance between messi and Ronaldo
def RadarChart(player, data, player2, data2):
    Attributes = ["Overall","Potential","Pace","Shooting","Passing","Dribbling","Defending","Physical"]

    data += data [:1]
    data2 += data2 [:1]
    
    angles = [n / 8 * 2 * pi for n in range(8)]
    angles += angles [:1]
    
    angles2 = [n / 8 * 2 * pi for n in range(8)]
    angles2 += angles2 [:1]
    plt.figure(dpi=125)
    ax = plt.subplot(111, polar=True)
    ax = plt.subplot(111, polar=True)
    
    plt.xticks(angles[:-1],Attributes)

    ax.plot(angles,values)
    ax.fill(angles, values, 'teal', alpha=0.1)

    ax.plot(angles2,values2)
    ax.fill(angles2, values2, 'red', alpha=0.1)

    plt.figtext(0.1,0.98,player,color="steelblue")
    plt.figtext(0.1,0.94,"vs")
    plt.figtext(0.1,0.9,player2,color="darkorange")
    
    
RadarChart("Messi",[94.0, 94.0, 87.0, 92.0, 92.0, 96.0, 39.0, 66.0, 94.0],"Ronaldo",[93.0, 93.0, 90.0, 93.0, 82.0, 89.0, 35.0, 78.0, 93.0]);


# Show some skills for Messi and M.Salah
df2=df[['short_name','overall','potential','pace','shooting','passing','dribbling','defending','physic']]
rival = df2.head(10)
rival= rival.set_index('short_name')
rival.head(10)


# Finding the row values of Messi
values1 = rival.iloc[0].tolist()
values1 += values [:1]

# Finding the row values of M. salah
values3 = rival.iloc[-1].tolist()
values3 += values2[:1]


# Plottin RadarChart to display the differance between messi and M. Salah
def RadarChart(player, data, player2, data2):
    Attributes = ["Overall","Potential","Pace","Shooting","Passing","Dribbling","Defending","Physical"]

    data += data [:1]
    data2 += data2 [:1]
    
    angles = [n / 8 * 2 * pi for n in range(8)]
    angles += angles [:1]
    
    angles2 = [n / 8 * 2 * pi for n in range(8)]
    angles2 += angles2 [:1]
    plt.figure(dpi=125)
    ax = plt.subplot(111, polar=True)
    ax = plt.subplot(111, polar=True)
    
    plt.xticks(angles[:-1],Attributes)

    ax.plot(angles,values1)
    ax.fill(angles, values1, 'teal', alpha=0.1)

    ax.plot(angles2,values3)
    ax.fill(angles2, values3, 'red', alpha=0.1)

    plt.figtext(0.1,0.98,player,color="steelblue")
    plt.figtext(0.1,0.94,"vs")
    plt.figtext(0.1,0.9,player2,color="darkorange")
    
    
RadarChart("Messi",[94.0, 94.0, 87.0, 92.0, 92.0, 96.0, 39.0, 66.0, 94.0],"M. Salah",[90.0, 90.0, 93.0, 86.0, 81.0, 89.0, 45.0, 74.0, 93.0]);


# Finding the row values of M.Salah
values7 = rival.iloc[-1].tolist()
values7 += values [:1]

# Finding the row values of Ronaldo
values8 = rival.iloc[1].tolist()
values8 += values2[:1]


# Plottin RadarChart to display the differance between messi and Ronaldo
def RadarChart(player, data, player2, data2):
    Attributes = ["Overall","Potential","Pace","Shooting","Passing","Dribbling","Defending","Physical"]

    data += data [:1]
    data2 += data2 [:1]
    
    angles = [n / 8 * 2 * pi for n in range(8)]
    angles += angles [:1]
    
    angles2 = [n / 8 * 2 * pi for n in range(8)]
    angles2 += angles2 [:1]
    plt.figure(dpi=125)
    ax = plt.subplot(111, polar=True)
    ax = plt.subplot(111, polar=True)
    
    plt.xticks(angles[:-1],Attributes)

    ax.plot(angles,values7)
    ax.fill(angles, values7, 'teal', alpha=0.1)

    ax.plot(angles2,values8)
    ax.fill(angles2, values8, 'red', alpha=0.1)

    plt.figtext(0.1,0.98,player,color="steelblue")
    plt.figtext(0.1,0.94,"vs")
    plt.figtext(0.1,0.9,player2,color="darkorange")
    
    
RadarChart("M. Salah",[90.0, 90.0, 93.0, 86.0, 81.0, 89.0, 45.0, 74.0, 93.0],"Ronaldo",[93.0, 93.0, 90.0, 93.0, 82.0, 89.0, 35.0, 78.0, 93.0]);


from scipy import stats

# Select overall , attacking finishing for preferred foot
a = df[df['preferred_foot'] == 'Left'][['overall', 'attacking_finishing']]
b = df[df['preferred_foot'] == 'Right'][['overall', 'attacking_finishing']]


print("Length of samples",len(a), len(b))
print("mean of overall rating and attacking_finishing for Left foot",a.mean(),
      "mean of overall rating and attacking_finishing for Right foot", b.mean())

Length of samples 4318 13960
mean of overall rating and attacking_finishing for Left foot overall                66.675776
attacking_finishing    46.922186
dtype: float64 mean of overall rating and attacking_finishing for Right foot overall                66.111748
attacking_finishing    45.178223
dtype: float64


# Take a equal samples from two overall data
new_a = a['overall'].sample(n=30)
new_b = a['overall'].sample(n=30)

# Calculate T-test , P-value for two samples
t , p = stats.ttest_ind(new_a, new_b, equal_var=False)
print("t = " + str(t))
print("p = " + str(p))

t = 0.0957031863709998
p = 0.9240929878306035


# Take a equal samples from two attacking finishing data
att_a = a['attacking_finishing'].sample(n=30)
att_b = a['attacking_finishing'].sample(n=30)

# Calculate T-test , P-value for two samples
t1, p1 = stats.ttest_ind(att_a,att_b, equal_var= False)
print("t = " + str(t1))
print("p = " + str(p1))

t = -1.4898245743934588
p = 0.14168895199770298


df1 = df[['club', 'overall','age', 'potential', 'value_eur', 'wage_eur', 'skill_moves', 'weak_foot','attacking_finishing','movement_sprint_speed','bmi']]


df_clubs = df1[(df.club =='Manchester City') |
               (df.club =='Liverpool') |
               (df.club =='Chelsea') |
               (df.club =='Manchester United') |
               (df.club =='Arsenal') |
               (df.club =='Leeds United') |
               (df.club =='Everton') |
               (df.club =='Aston Villa') |
               (df.club =='Newcastle United') |
               (df.club =='Wolverhampton Wanderers') |
               (df.club == 'Tottenham Hotspur') |
              (df.club =='Leiceter City') |
               (df.club =='West Ham United') |
               (df.club =='Burnley') |
                  (df.club =='Southampton') |
               (df.club == 'Crystal Palace') |
              (df.club == 'Brighton & Hove Albion') |
               (df.club == 'West Bromwich Albion') |
               (df.club == 'Fulham') |
               (df.club == 'Sheffield United')]


df_clubs.club = df_clubs.club.replace({'Manchester City':'Top 6',
                                       'Manchester United': 'Top 6' ,
                                       'Liverpool':'Top 6',
                                       'Chelsea':'Top 6',
                                       'West Ham United':'Top 6',
                                       'Leiceter City':'Top 6',
                                       'Tottenham Hotspur':'Mid 11',
                                       'Arsenal':'Mid 11',
                                       'Leeds United':'Mid 11',
                                       'Everton':'Mid 11',
                                       'Aston Villa':'Mid 11',
                                       'Newcastle United':'Mid 11',
                                       'Wolverhampton Wanderers':'Mid 11',
                                       'Crystal Palace':'Mid 11',
                                       'Southampton':'Mid 11',
                                       'Brighton & Hove Albion':'Mid 11',
                                       'Burnley':'Mid 11',
                                       'Fulham':'Bot 3',
                                       'West Bromwich Albion':'Bot 3',
                                       'Sheffield United':'Bot 3'})


df_clubs.club.value_counts()

Mid 11    360
Top 6     165
Bot 3      93
Name: club, dtype: int64


df_clubs


df_clubs.shape

(618, 11)


# USe seaborn package to plot scatter plot
ax = sns.catplot( data= df_clubs, x='club', y ='overall', kind= 'swarm',size=7);

# Edit title , x label and y label
plt.title(label='Overall vs Player in Premier League Clubs', fontsize=20);
plt.xlabel(xlabel='Player in Clubs');
plt.ylabel(ylabel='Overall');


# USe seaborn package to plot scatter plot
ax = sns.catplot( data= df_clubs, x='club', y ='wage_eur', kind= 'swarm',size=7);

# Edit title , x label and y label
plt.title(label='Wage vs Player in Premier League Clubs', fontsize=20);
plt.xlabel(xlabel='Player in Club');
plt.ylabel(ylabel='Wage');


# USe seaborn package to plot scatter plot
value_e=(df_clubs.value_eur)/1000000
ax = sns.catplot( data= df_clubs, x='potential', y =value_e ,hue='club', kind= 'swarm',size=7);

# Edit title , x label and y label
plt.title(label='Potential vs Value colored by Player in Premier League Clubs', fontsize=20);
plt.xlabel(xlabel='Potential');
plt.ylabel(ylabel='Value');


X = df_clubs.iloc[:, 1:].values
y = df_clubs.club.values


from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state = 1)


from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)

StandardScaler()


X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)


from sklearn.tree import DecisionTreeClassifier


dtc = DecisionTreeClassifier()


dtc.fit(X_train, y_train)

DecisionTreeClassifier()


y_pred = dtc.predict(X_test)


from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.model_selection import cross_val_score


pd.crosstab(y_test, y_pred, rownames=['True'], colnames=['Predicted'], margins=True)


from sklearn.metrics import plot_confusion_matrix
plot_confusion_matrix(dtc, X_test, y_test);


accuracy = accuracy_score(y_test, y_pred)
print("Accuracy : " , accuracy)

Accuracy :  0.6048387096774194


X_train1, X_test1, y_train1, y_test1 = train_test_split(X, y, test_size=0.20, random_state = 1)


from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train1 = sc.fit_transform(X_train1)
X_test1 = sc.transform(X_test1)


from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 3)


classifier.fit(X_train1, y_train1)

KNeighborsClassifier(p=3)


y_pred1 = classifier.predict(X_test1)


from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test1, y_pred1)
print(cm)

[[ 8 13  0]
 [ 9 52  9]
 [ 1 21 11]]


from sklearn.metrics import plot_confusion_matrix
plot_confusion_matrix(classifier, X_test1, y_test1);


accuracy1=accuracy_score(y_test1, y_pred1)
print('Accuracy :' ,accuracy1 )

Accuracy : 0.5725806451612904


from sklearn.model_selection import train_test_split
X_train2, X_test2, y_train2, y_test2 = train_test_split(X, y, test_size = 0.25, random_state = 0)


from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train2 = sc.fit_transform(X_train2)
X_test2= sc.transform(X_test2)


from sklearn.svm import SVC
classifier = SVC(kernel = 'linear', random_state = 0)


classifier.fit(X_train2, y_train2)

SVC(kernel='linear', random_state=0)


y_pred2 = classifier.predict(X_test2)


from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test2, y_pred2)
print(cm)

[[ 0 26  0]
 [ 0 85 11]
 [ 0 17 16]]


plot_confusion_matrix(classifier, X_test2, y_test2);


accuracy2 =accuracy_score(y_test2, y_pred2)
print('Accuracy :',accuracy2)

Accuracy : 0.6516129032258065


from sklearn.model_selection import train_test_split
X_train3, X_test3, y_train3, y_test3 = train_test_split(X, y, test_size = 0.25, random_state = 0)


from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train3 = sc.fit_transform(X_train3)
X_test3 = sc.transform(X_test3)


from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)


classifier.fit(X_train3, y_train3)

RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=0)


y_pred3 = classifier.predict(X_test3)


from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test3, y_pred3)
print(cm)

[[12 14  0]
 [ 7 75 14]
 [ 0 13 20]]


from sklearn.metrics import plot_confusion_matrix
plot_confusion_matrix(classifier, X_test3, y_test3);


accuracy3 =accuracy_score(y_test3, y_pred3)
print('Accuracy :' , accuracy3)

Accuracy : 0.6903225806451613

	sofifa_id	age	height_cm	weight_kg	overall	potential	value_eur	wage_eur	international_reputation	weak_foot	...	mentality_penalties	mentality_composure	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes
count	18278.000000	18278.000000	18278.000000	18278.000000	18278.000000	18278.000000	1.827800e+04	18278.000000	18278.000000	18278.000000	...	18278.000000	18278.000000	18278.000000	18278.000000	18278.000000	18278.000000	18278.000000	18278.000000	18278.000000	18278.000000
mean	219738.864482	25.283291	181.362184	75.276343	66.244994	71.546887	2.484038e+06	9456.942773	1.103184	2.944250	...	48.383357	58.528778	46.848889	47.640333	45.606631	16.572765	16.354853	16.212934	16.368038	16.709924
std	27960.200461	4.656964	6.756961	7.047744	6.949953	6.139669	5.585481e+06	21351.714095	0.378861	0.664656	...	15.708099	11.880840	20.091287	21.585641	21.217734	17.738069	16.996925	16.613665	17.136497	18.038125
min	768.000000	16.000000	156.000000	50.000000	48.000000	49.000000	0.000000e+00	0.000000	1.000000	1.000000	...	7.000000	12.000000	1.000000	5.000000	3.000000	1.000000	1.000000	1.000000	1.000000	1.000000
25%	204445.500000	22.000000	177.000000	70.000000	62.000000	67.000000	3.250000e+05	1000.000000	1.000000	3.000000	...	39.000000	51.000000	29.000000	27.000000	24.000000	8.000000	8.000000	8.000000	8.000000	8.000000
50%	226165.000000	25.000000	181.000000	75.000000	66.000000	71.000000	7.000000e+05	3000.000000	1.000000	3.000000	...	49.000000	60.000000	52.000000	55.000000	52.000000	11.000000	11.000000	11.000000	11.000000	11.000000
75%	240795.750000	29.000000	186.000000	80.000000	71.000000	75.000000	2.100000e+06	8000.000000	1.000000	3.000000	...	60.000000	67.000000	64.000000	66.000000	64.000000	14.000000	14.000000	14.000000	14.000000	14.000000
max	252905.000000	42.000000	205.000000	110.000000	94.000000	95.000000	1.055000e+08	565000.000000	5.000000	5.000000	...	92.000000	96.000000	94.000000	92.000000	90.000000	90.000000	92.000000	93.000000	91.000000	92.000000

	sofifa_id	player_url	short_name	long_name	age	dob	height_cm	weight_kg	nationality	club	...	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
0	158023	https://sofifa.com/player/158023/lionel-messi/...	L. Messi	Lionel Andrés Messi Cuccittini	32	1987-06-24	170	72	Argentina	FC Barcelona	...	68+2	66+2	66+2	66+2	68+2	63+2	52+2	52+2	52+2	63+2
1	20801	https://sofifa.com/player/20801/c-ronaldo-dos-...	Cristiano Ronaldo	Cristiano Ronaldo dos Santos Aveiro	34	1985-02-05	187	83	Portugal	Juventus	...	65+3	61+3	61+3	61+3	65+3	61+3	53+3	53+3	53+3	61+3
2	190871	https://sofifa.com/player/190871/neymar-da-sil...	Neymar Jr	Neymar da Silva Santos Junior	27	1992-02-05	175	68	Brazil	Paris Saint-Germain	...	66+3	61+3	61+3	61+3	66+3	61+3	46+3	46+3	46+3	61+3
3	200389	https://sofifa.com/player/200389/jan-oblak/20/...	J. Oblak	Jan Oblak	26	1993-01-07	188	87	Slovenia	Atlético Madrid	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	183277	https://sofifa.com/player/183277/eden-hazard/2...	E. Hazard	Eden Hazard	28	1991-01-07	175	74	Belgium	Real Madrid	...	66+3	63+3	63+3	63+3	66+3	61+3	49+3	49+3	49+3	61+3

	sofifa_id	player_url	short_name	long_name	age	dob	height_cm	weight_kg	nationality	club	...	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
18273	245006	https://sofifa.com/player/245006/shuai-shao/20...	Shao Shuai	邵帅	22	1997-03-10	186	79	China PR	Beijing Renhe FC	...	43+2	42+2	42+2	42+2	43+2	45+2	46+2	46+2	46+2	45+2
18274	250995	https://sofifa.com/player/250995/mingjie-xiao/...	Xiao Mingjie	Mingjie Xiao	22	1997-01-01	177	66	China PR	Shanghai SIPG FC	...	44+2	43+2	43+2	43+2	44+2	46+2	47+2	47+2	47+2	46+2
18275	252332	https://sofifa.com/player/252332/wei-zhang/20/...	Zhang Wei	张威	19	2000-05-16	186	75	China PR	Hebei China Fortune FC	...	47+2	49+2	49+2	49+2	47+2	47+2	49+2	49+2	49+2	47+2
18276	251110	https://sofifa.com/player/251110/haijian-wang/...	Wang Haijian	汪海健	18	2000-08-02	185	74	China PR	Shanghai Greenland Shenhua FC	...	48+2	48+2	48+2	48+2	48+2	48+2	49+2	49+2	49+2	48+2
18277	233449	https://sofifa.com/player/233449/ximing-pan/20...	Pan Ximing	潘喜明	26	1993-01-11	182	78	China PR	Hebei China Fortune FC	...	48+2	49+2	49+2	49+2	48+2	48+2	50+2	50+2	50+2	48+2

	Columns	Value	Proportion
0	player_tags	16779	91.798884
1	gk_speed	16242	88.860926
2	gk_positioning	16242	88.860926
3	gk_reflexes	16242	88.860926
4	gk_kicking	16242	88.860926
...	...	...	...
64	attacking_volleys	0	0.000000
65	attacking_short_passing	0	0.000000
66	attacking_heading_accuracy	0	0.000000
67	attacking_finishing	0	0.000000
68	short_name	0	0.000000

	overall	potential	pace	shooting	passing	dribbling	defending	physic
short_name
L. Messi	94	94	87.0	92.0	92.0	96.0	39.0	66.0
Cristiano Ronaldo	93	93	90.0	93.0	82.0	89.0	35.0	78.0

	club	overall	age	potential	value_eur	wage_eur	skill_moves	weak_foot	attacking_finishing	movement_sprint_speed	bmi
5	Top 6	91	28	91	90000000	370000	4	5	82	76	21.0
7	Top 6	90	27	91	78000000	200000	2	3	52	79	24.0
9	Top 6	90	27	90	80500000	240000	4	3	90	92	23.0
12	Mid 11	89	25	91	83000000	220000	3	4	94	72	25.0
13	Top 6	89	26	91	58000000	155000	1	3	13	47	24.0
...	...	...	...	...	...	...	...	...	...	...	...
17585	Mid 11	53	18	76	130000	3000	2	3	36	62	21.0
17645	Mid 11	53	20	69	130000	5000	3	3	49	63	21.0
17655	Mid 11	53	19	68	110000	2000	1	2	9	37	23.0
17852	Mid 11	52	19	67	110000	4000	2	3	28	62	21.0
18023	Bot 3	51	17	65	70000	1000	2	2	52	68	22.0

	overall	potential	pace	shooting	passing	dribbling	defending	physic
short_name
L. Messi	94	94	87.0	92.0	92.0	96.0	39.0	66.0
Cristiano Ronaldo	93	93	90.0	93.0	82.0	89.0	35.0	78.0
Neymar Jr	92	92	91.0	85.0	87.0	95.0	32.0	58.0
J. Oblak	91	93	0.0	0.0	0.0	0.0	0.0	0.0
E. Hazard	91	91	91.0	83.0	86.0	94.0	35.0	66.0
K. De Bruyne	91	91	76.0	86.0	92.0	86.0	61.0	78.0
M. ter Stegen	90	93	0.0	0.0	0.0	0.0	0.0	0.0
V. van Dijk	90	91	77.0	60.0	70.0	71.0	90.0	86.0
L. Modrić	90	90	74.0	76.0	89.0	89.0	72.0	66.0
M. Salah	90	90	93.0	86.0	81.0	89.0	45.0	74.0

FIFA 20 Dataset Analysis¶

Table of content¶

Our Aim¶

Introduction¶

Questions:¶

Load the packages¶

Data Wrangling¶

Describe statistic for data¶

Attributes of dataset¶

Check duplicated rows¶

What is the structure of your dataset?¶

What is/are the main feature(s) of interest in your dataset?¶

What features in the dataset do you think will help support your investigation into your feature(s) of interest?¶

Data Preprocessing¶

Data Cleaning¶

Define¶

Code :¶

Test :¶

Define¶

Code :¶

Check for missing values¶

Plotting the percentage of missing values¶

Test :¶

Add New Feature¶

Body Mass Index :¶

Code :¶

Data Exploration¶

Questions¶

Univariate Exploration¶

Question 1 :¶

Distributed of some numerical attributes.¶

Code :¶

we can more display for the distributions by boxplot¶

Code :¶

Question 2 :¶

Count of players by age.¶

Code :¶

Question 3 :¶

Count of players by nationality.¶

Code :¶

Question 4 :¶

Count of players by preferred foot.¶

Code :¶

Question 5 :¶

Count of players by BMI(Body Mass Index).¶

Code :¶

Question 6 :¶

Players have the max values in some feature(overall ,age, potential, value_eur, wage_eur,etc..)¶

Code :¶

Question 7 :¶

Clubs have top 20 players with a high overall.¶

Code :¶

Question 8 :¶

Nationality have top 10 players with a high overall.¶

Code :¶

Question 9 :¶

Age has Max players in Top 20¶

Code :¶

Question 10 :¶

Proportion of Player's per Position¶

Percentage of players in Defender Role¶

Code :¶

Percentage of players in Midfielder Role¶

Code :¶

Percentage of players in Attacker Role¶

Code :¶

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?¶

Bivariate Exploration¶

Question 11 :¶

Does increasing of overall rating depend on age?¶

Code :¶

Question 12 :¶

Does increasing in overall depend on body mass index (BMI) ?¶

Code :¶

Question 13 :¶

Relation between height and weight.¶

Code :¶

Question 14 :¶

Relation between weight and pace.¶

Code :¶